Agent Architecture
The Purpose Driven Agent framework implements a modular architecture that combines specialized agent types to create comprehensive AI capabilities.
Core Components
Learning Agent
Implements continuous learning capabilities, allowing the agent to improve from experience and adapt to new situations.
- Pattern recognition
- Adaptive behavior
- Experience-based optimization
Knowledge Agent
Manages information gathering, storage, and retrieval to build and maintain a comprehensive knowledge base.
- Information organization
- Context-aware retrieval
- Knowledge integration
Intelligence Agent
Provides reasoning, planning, and decision-making capabilities based on available knowledge and objectives.
- Logical reasoning
- Strategic planning
- Goal-oriented decisions
Coder Agent
Specialized implementation that focuses on software development tasks and code generation.
- Code generation
- Best practice implementation
- Testing and validation
Implementation Example
Creating a specialized agent using the Purpose Driven Agent framework is straightforward through inheritance and customization.
from PurposeDrivenAgent.CoderAgent import CoderAgent
class CustomCodingAssistant(CoderAgent):
def __init__(self, specialization="web_development"):
"""
Initialize a custom coding assistant with a specific specialization.
:param specialization: Area of coding expertise, e.g., web_development, data_science
"""
super().__init__() # Initialize the base CoderAgent
self.specialization = specialization
def generate_solution(self, problem_description):
"""
Generate code solution based on problem description.
:param problem_description: Description of the coding problem
:return: Generated code solution
"""
# Custom implementation for solution generation
solution = self._analyze_problem(problem_description)
code = self._generate_code(solution)
return self._validate_and_optimize(code)
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